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import math
from abc import abstractmethod, ABCMeta
from functools import cached_property
from typing import Sequence, Any, Dict, Callable, Tuple
import xarray as xr
from xcube.core.gridmapping import GridMapping
from xcube.core.tilingscheme import TilingScheme
from xcube.util.types import ScalarOrPair
from xcube.util.types import normalize_scalar_or_pair
[docs]
class MultiLevelDataset(metaclass=ABCMeta):
"""
A multi-level dataset of decreasing spatial resolutions
(a multi-resolution pyramid).
The pyramid level at index zero provides the original spatial dimensions.
The size of the spatial dimensions in subsequent levels
is computed by the formula ``size[index + 1] = (size[index] + 1) // 2``
with ``size[index]`` being the maximum size of the spatial dimensions
at level zero.
Any dataset chunks are assumed to be the same in all levels. Usually,
the number of chunks is one in one of the spatial dimensions of the
highest level.
"""
@property
@abstractmethod
def ds_id(self) -> str:
"""
:return: the dataset identifier.
"""
@ds_id.setter
@abstractmethod
def ds_id(self, ds_id: str):
"""
Set the dataset identifier.
"""
@property
@abstractmethod
def grid_mapping(self) -> GridMapping:
"""
:return: the CF-conformal grid mapping
"""
@property
@abstractmethod
def num_levels(self) -> int:
"""
:return: the number of pyramid levels.
"""
@cached_property
def resolutions(self) -> Sequence[Tuple[float, float]]:
"""
:return: the x,y resolutions for each level given in the
spatial units of the dataset's CRS
(i.e. ``self.grid_mapping.crs``).
"""
x_res_0, y_res_0 = self.grid_mapping.xy_res
return [(x_res_0 * (1 << level), y_res_0 * (1 << level))
for level in range(self.num_levels)]
@cached_property
def avg_resolutions(self) -> Sequence[float]:
"""
:return: the average x,y resolutions for each level given in the
spatial units of the dataset's CRS
(i.e. ``self.grid_mapping.crs``).
"""
x_res_0, y_res_0 = self.grid_mapping.xy_res
xy_res_0 = math.sqrt(x_res_0 * y_res_0)
return [xy_res_0 * (1 << level)
for level in range(self.num_levels)]
@property
def base_dataset(self) -> xr.Dataset:
"""
:return: the base dataset for lowest level at index 0.
"""
return self.get_dataset(0)
@property
def datasets(self) -> Sequence[xr.Dataset]:
"""
Get datasets for all levels.
Calling this method will trigger any lazy dataset instantiation.
:return: the datasets for all levels.
"""
return [self.get_dataset(index) for index in range(self.num_levels)]
[docs]
@abstractmethod
def get_dataset(self, index: int) -> xr.Dataset:
"""
:param index: the level index
:return: the dataset for the level at *index*.
"""
[docs]
def close(self):
""" Close all datasets. Default implementation does nothing. """
[docs]
def apply(self,
function: Callable[[xr.Dataset, Dict[str, Any]], xr.Dataset],
kwargs: Dict[str, Any] = None,
ds_id: str = None) -> 'MultiLevelDataset':
""" Apply function to all level datasets
and return a new multi-level dataset.
"""
from .mapped import MappedMultiLevelDataset
return MappedMultiLevelDataset(self, function,
ds_id=ds_id,
mapper_params=kwargs)
[docs]
def derive_tiling_scheme(self, tiling_scheme: TilingScheme):
"""
Derive a new tiling scheme for the given one with defined
minimum and maximum level indices.
"""
min_level, max_level = tiling_scheme.get_levels_for_resolutions(
self.avg_resolutions,
self.grid_mapping.spatial_unit_name
)
return tiling_scheme.derive(min_level=min_level,
max_level=max_level)
[docs]
def get_level_for_resolution(self, xy_res: ScalarOrPair[float]) -> int:
"""
Get the index of the level that best represents the given resolution.
:param xy_res: the resolution in x- and y-direction
:return: a level ranging from 0 to self.num_levels - 1
"""
given_x_res, given_y_res = normalize_scalar_or_pair(xy_res,
item_type=float)
for level, (x_res, y_res) in enumerate(self.resolutions):
if x_res > given_x_res and y_res > given_y_res:
return max(0, level - 1)
return self.num_levels - 1